Description Usage Arguments Details Value See Also Examples

`btmodel`

is a basic fitting function for simple Bradley-Terry models.

1 2 3 |

`y` |
paircomp object with the response. |

`weights` |
an optional vector of weights (interpreted as case weights). |

`type` |
character. Should an auxiliary log-linear Poisson model or logistic binomial be employed for estimation? The latter is not available if undecided effects are estimated. |

`ref` |
character or numeric. Which object parameter should be the reference category, i.e., constrained to zero? |

`undecided` |
logical. Should an undecided parameter be estimated? |

`position` |
logical. Should a position effect be estimated? |

`start` |
numeric. Starting values when calling |

`vcov` |
logical. Should the estimated variance-covariance be included in the fitted model object? |

`estfun` |
logical. Should the empirical estimating functions (score/gradient contributions) be included in the fitted model object? |

`...` |
further arguments passed to functions. |

`btmodel`

provides a basic fitting function for Bradley-Terry models,
intended as a building block for fitting Bradley-Terry trees and
Bradley-Terry mixtures in the psychotree package, respectively. While
`btmodel`

is intended for individual paired-comparison data, the
eba package provides functions for aggregate data.

`btmodel`

returns an object of class `"btmodel"`

for which
several basic methods are available, including `print`

, `plot`

,
`summary`

, `coef`

, `vcov`

, `logLik`

, `estfun`

and `worth`

.

`btmodel`

returns an S3 object of class `"btmodel"`

,
i.e., a list with components as follows.

`y` |
paircomp object with the response |

`coefficients` |
estimated parameters on log-scale (without the first parameter which is always constrained to be 0), |

`vcov` |
covariance matrix of the parameters in the model, |

`loglik` |
log-likelihood of the fitted model, |

`df` |
number of estimated parameters, |

`weights` |
the weights used (if any), |

`n` |
number of observations (with non-zero weights), |

`type` |
character for model type (see above), |

`ref` |
character for reference category (see above), |

`undecided` |
logical for estimation of undecided parameter (see above), |

`position` |
logical for estimation of position effect (see above), |

`labels` |
character labels of the objects compared, |

`estfun` |
empirical estimating function (also known as scores or gradient contributions). |

`pcmodel`

, `rsmodel`

,
`raschmodel`

, the eba package

1 2 3 4 5 6 7 8 9 10 11 |

```
Bradley-Terry regression model
Parameters:
Estimate Std. Error z value Pr(>|z|)
none -0.3756 0.0890 -4.22 2.5e-05 ***
Linke -0.6161 0.0910 -6.77 1.3e-11 ***
Gruene 1.1858 0.0951 12.47 < 2e-16 ***
SPD 0.8131 0.0907 8.97 < 2e-16 ***
CDU/CSU 0.1756 0.0875 2.01 0.045 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Log-likelihood: -1720 (df = 5)
```

psychotools documentation built on May 17, 2018, 9:04 a.m.

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